Annotation of multimedia contents (such as Audio/Video contents or written articles) with additional information (so called metadata) is increasingly becoming a main issue to cope with, due to the massive amount of available data. Such metadata may come from professional sources, but may also come from user sources, such as social networks and web forums.
To face with such a huge amount of data, there is a need to identify and to filter the most relevant additional information to a given multimedia content, such pieces of information being retrieved, for instance, from social networks over the Internet, Web forum, dedicated websites, etc.
Prior art solutions often implement search engines using keywords or fixed taxonomy, to browse and to access metadata associated to a given multimedia content over the Internet. Nevertheless, current search engines do not take into account the relevance of the metadata before delivering them to requesting users, but only use requested keywords in or related to the given multimedia content.
In addition, it is also known to select, for a given multimedia content published on-line, the related metadata approved by a large majority of web users thanks to, for instance, “plus on” or “like” actions. However, such an identification of relevant metadata requires interactions of other web users.
Thus, there remains a significant need for identifying the most interesting metadata in order to enrich a given multimedia content.